SKEW should lead VIX, right? Traders get worried about a crash, which might anticipate volatility in the S&P 500.

SKEW is in green.VIX is in blue.

If SKEW was a perfect predictor of VIX, then you’d expect the blue line to look like the green line with a small gap in between them.

The theory was that SKEW (in green) would pull up VI (in blue).

A quick scan of the chart shows that’s not the case. There are occasions where green spikes up followed by blue, but it intuitively feels to me like a case of cherry picking. Also, notice the largest blue spike around value 500. If SKEW lagged VIX of the other way around.

Just for the sake of being thorough, I measured the cross correlations of If SKEW and VIX using both my smoothed values and the unprocessed ones.

Here’s the cross correlation of the smoothed values.

The cross correlation of smoothed SKEW and VIX

And here’s the cross correlation of the unsmoothed values.

If anything, the hypothesis is backwards. SKEW 18 days ahead of VIX has a -19% correlation. The correlation should be positive and > 40% to carry any substantial meaning. The weak correlation value and the fact that it’s negative that this idea is better tossed in the bin.

Click here to download the data used in this analysis. You’ll noticed that I first normalized the VIX and SKEW values to allow for easier visual comparisions. Because the data is extremely noisy, I applied a 7 day SMA to make visual comparisons easier.

The data used was from October 16, 2013 to December 21, 2017.

What is SKEW?

SKEW, which is another volatility index run by the CBOE, provides a measure of how worried traders are about tail risks.

The crash of October 1987 sensitized investors to the potential for stock market crashes and forever changed their view of S&P 500® returns. Investors now realize that S&P 500 tail risk – the risk of outlier returns two or more standard deviations below the mean – is significantly greater than under a lognormal distribution. The Cboe SKEW Index (“SKEW”) is an index derived from the price of S&P 500 tail risk. Similar to VIX®, the price of S&P 500 tail risk is calculated from the prices of S&P 500 out-of-the-money options.

SKEW typically ranges from 100 to 150. A SKEW value of 100 means that the perceived distribution of S&P 500 log-returns is normal, and the probability of outlier returns is therefore negligible. As SKEW rises above 100, the left tail of the S&P 500 distribution acquires more weight, and the probabilities of outlier returns become more significant. One can estimate these probabilities from the value of SKEW. Since an increase in perceived tail risk increases the relative demand for low strike puts, increases in SKEW also correspond to an overall steepening of the curve of implied volatilities, familiar to option traders as the “skew”.

What is VIX?

The Cboe Volatility Index® (VIX® Index) is considered by many to be the world’s premier barometer of equity market volatility. The VIX Index is based on real-time prices of options on the S&P 500® Index (SPX) and is designed to reflect investors’ consensus view of future (30-day) expected stock market volatility. The VIX Index is often referred to as the market’s “fear gauge”.

The VIX Index is the centerpiece of Cboe Global Markets’ volatility franchise, which includes volatility indexes on broad-based stock indexes, exchange traded funds, individual stocks, commodities and several strategy and performance based indexes, as well as tradable volatility contracts, such as VIX options and futures.

These revolutionary volatility products can offer investors effective ways to help manage risk, leverage volatility and diversify a portfolio.

December and January were extremely unkind to me. I took a huge loss on December 9 that coincided with the Fed meeting and another big punch in January. In total, I went from a 28% profit to a ~4% net loss.

Deservedly, my inbox quickly flooded with comments and suggestions on the drawdown. The most common of those was to stop trading during news events.

So… why am I still trading during news events? There are a few answers to that question.

Curve fitting

It’s not like the strategy loses money on every single news event. It’s 100% true that news events like the Fed meeting can and badly hurt. Say that I’m determined to exclude news events in the future. I’d have to

Collect historical news event data

Create a second algorithm, which selects the news events that forbid and allow trading to continue

Test how the news algorithm interacts with Dominari

Repeat this many times until I’m happy with the final result

Due to the tiny number of news events that impact the markets like the December 9th announcement, my data set is miniature. The risk of overfitting to historical news events is huge.

Working with tiny amounts of data provides little in the way of long run confidence. Focusing my efforts elsewhere is far more likely to improve performance and requires much less work.

Too many trades

Too many trades sounds a bit naive, so let’s dig into what that means. Dominari trades a portfolio of 7 different instruments. All instruments cross with USD.

EURUSD

GBPUSD

USDCHF

AUDUSD

NZDUSD

USDJPY

USDCAD

Many subscribers correctly observed that the major losses occurred with trades open on all 7 pairs in the portfolio at the same time. A good predictor of trade performance is the number of trades open simultaneously.

Testing and confirming the max open trades rule was quick and easy. 5+ trades is very dangerous.

Accordingly, Dominari now exits all open trades if there are 5 or more trades open at any given time.

The next feature of Dominari will be a reversal strategy. Dominari was clearly prone to sudden equity changes if 5+ trades were open at the same time.

Make the losses work for us

An obvious counter strategy is to open trades in the opposite direction whenever Dominari would otherwise open too many trades. Testing the idea is very easy.

Coding a Dominari reversal strategy, however, would require a major reprogramming of the expert advisor’s code.

The number of trades per year would be miniscule. I doubt that it would average even 1 trade per month.

The idea is that Dominari can be the normal trading strategy. Whenever Dominari opens too many trades, the strategy then switches into reversal mode and trend trades with a simple trailing stop.

Switching direction should mostly reverse the negative trade skewness back in the positive direction. Almost all of the offending trades open at exactly the same time.

If the biggest losing trades opened at different times, there would be the risk of being too late to the party. All blowout trades opening at the same time means that the strategy can realistically reverse 100% of would-be losses into profits.

Sitting at the top of the docket are changes to Pilum. You can expect to hear about those soon so that I can incorporate Pilum into the Dominari signals. Once that and 2 other internal projects are finished, I’ll be able to dedicate the time required to fully implement the Dominari Reversal System.

Equity stop loss

Dominari uses emergency stop losses on all tickets. That is appropriate 99% of the time for individual trades. Those emergency losses reset once per hour in line with the concept of the TODS.

A little of the problem was bad luck. My stops came within a handful of pips of being triggered. Then they reset even further away, which made a bad problem worse.

When all trades move at the same time, then clearly the strategy could suffer extreme losses.

The first attempted solution after the Fed announcement was to add a portfolio level stop loss. The way that I wrote it also updated once per hour. When a second negative movement came in January, I stopped trying to be clever. It’s a flat, simple, stupid stop loss. If I lose more than 4% on all open trades, the entire Dominari portfolio goes flat.

I’m still trading Dominari

I still have my money trading the Dominari system; my confidence in the long term performance hasn’t changed, but it obviously requires safeguards. The max number of trades and the portfolio level stop loss will go a long way to limiting the impact of big moves in the future. AND, I should get the counter-strategy developed relatively soon to turn potential frowns upside down.

Lastly, many of you questioned why I’ve been so quiet. The honest answer is that I needed some time to process what happened. It’s easy to feel overwhelmed and discouraged when you get knocked down. I needed some time to process what happened.

I also needed time to double check the changes that I made to the portfolio were actually beneficial. It’s very easy to appease traders when they’re upset by rushing out features before they’re thoughtfully considered.

My money is on the line (I lost 2,000 euros between the two moves). What hurt my subscribers hurt me, too.

I was really excited about my Pilum strategy two months ago. The research looked great and everything was ready to rock and roll. Demo testing began and then… not much happened.

The Quantilator is (mostly) finished, which finally gave me time to circle back and review what happened with Pilum.

Live demo trading of Pilum. Dec 9, 2016 to Feb 7, 2017

The expected outcome was that I would win 75% of the time. Trades were infrequent, so I thought maybe I’m just having bad luck. But then my win rate remained stuck around 50%. Simple statistical tests told me this was unlikely to be bad luck.

I used the research time to pour over my research code and to compare it with live trades. What I found was that a single line of code (AHHHHHHHHHHHHHHH!) was incorrectly calculating my entry price, dramatically overstating the profits.

The flawed code produced this equity curve from a single combination of settings:

When the actual, correct result looks like this with those same settings:

The accurate backtest of Pilum

I’ll be honest… I like the flawed backtest a lot more!

The new, single-setting backtest isn’t as good, but it’s still trade-worthy. There are some characteristics that I dislike and features that I love. Let’s dig into those.

What I dislike

The frequency of trades is very low. Out of 19 months there were a total of 43 trades. 43 trades to comprise a backtest on 40+ instruments is a very small number.

If it weren’t for the statistical pattern backing up the frequency, I would not consider the test. However, there are 20,000 bars each on the 44 instruments. There are 880,000 total bars used to analyze whether my Pilum pattern offers any predictive value.

The most valuable predictions, however, are also exceptionally rare. That’s why I’m not able to get the trading frequency higher, which would potentially smooth the returns.

What I love

Now look again at the correct equity curve (the image to the right). Do you see the final profit of roughly 0.14? That’s a 14% unleveraged return over a 19 month period.

Allocating 2:1 or 3:1 leverage on this strategy could average annual returns of 15-25%.

Detecting hidden risk

A key measure of risk is skewness. You may not use that term yourself, but it’s something most of you already understand. The biggest complaint about people trading Dominari was that the average winner relative to the average loser was heavily skewed towards the losers.

Dominari wins on most months, but when it lost in December it was devastating. I implemented what I thought was a portfolio stop after the December 9th aftermath. Then I had a smaller, but still very painful, loss in January. The portfolio level stop loss of 3% should prevent future blowouts now that I know what goes wrong.

I still believe in Dominari. But, I obviously lost the work of most of the year due to those events.

Knowing that skewness is a good measure of blowout risk (even if you’ve never seen it in a backtest, like happened with Dominari), Pilum looks extremely encouraging.

This is a histogram of profit and loss by days. You should notice a few things.

The tallest bar is to the right of 0. That means that the most frequent outcome is winning.

The biggest winning day is dramatically better than the worst losing day. The worst outcome was a loss of 2%. The best outcome is gains near 10% in a single day (unleveraged!).

This is the statistical profile of an idea that’s much more likely to grab an avalanche of profits than it is to get blown out.

It gets even better

Would you say that the blue and red equity curves are highly or loosely correlated? Look closely.

Writing this blog post made me think carefully about the Pilum strategy. I decided that maybe I should see if all of the profits are coming from different settings at the same time. There’s very little risk of overfitting the data as my strategy only has 1 degree of freedom.

The blue bars are the equity curve of Setting 1.

The red bars are for Setting 2.

Do you think these are tightly or loosely correlated?

If you said loosely correlated, then you are correct. Notice how each equity curve shows large jumps of profit. Did you notice how those profit jumps occur on different days?

The blue setting skyrockets on a single day in November 2016. It leaves the red equity curve choking in its dust.

But then, look what happens as I advance into December. The red curve dramatically catches up to the blue curve and even overtakes it.

The correlation between the 2 strategies is only 57%.

Combine multiple settings into 1 portfolio

This is a much nicer equity curve!

Loose correlations are a GIFT. Combining two bumpy equity curves into a single strategy makes the performance much, much smoother.

The percentages of days that are profitable also increases. Setting 1 is profitable on 58.0% of days. Setting 2 is profitable on 53.5% of days.

But… combining them makes Pilum profitable on 68.2% of days. Awesome!

That also provides more data, which puts me in a stronger position to analyze the strategy’s skewness. Look at the frequency histograms below. They’re the same type of histograms that I showed you in the first section of this blog post. As you’ll notice, they look a lot different.

The most probable outcome for any given day is a small winner

The tall green bar is the most probable trading outcome for any given day with filled orders. The average day is a positive return of 0-1%.

The small red bar is the worst trading day of the combined strategy.

The small green bars are the best trading days of the combined strategy.

Look how far to the right the green bars go. The largest winner is more than 3x the biggest loss. And, there are so many more large winners compared to losers.

Giant winners are far more likely than comparable losses.

The Plan

I immediately pushed Pilum into live trading this combination of two strategies. I expect that adding a second degree of freedom and running about 30 different versions of the strategy – all with different settings – will add to the performance and smooth the returns even further.

Dominari hasn’t been working on my FXCM account, which is very difficult to accept because the lacking performance seems to be a buried execution issue. Pilum, however, trades very infrequently. It’s unlikely that execution quality will make a dramatic difference in the long term outcomes.

So, I’m going to convert the FXCM account to trading Pilum exclusively. That will be offered as a strategy on Collective2 within the next few weeks, a company with whom I’ve been working closely. Their users are more investor rather than trading oriented – they’re far more likely to view low trading frequency as a good thing. I suspect that most people here have a different opinion and want to see a lot of market action.

Reaching an all-time high in my equity curve means it’s time to buckle down and keep improving. My Dominari strategy has done very well over the past 7 months and especially this and last month.

Is the party going to continue?

I certainly expect so. Drawdowns are inevitable, but that’s part of trading. Short-term performance is exciting, but my ambitious goal is to turn my starting balance of €8,000 into €50,000 within the next 3 years. As of this writing, I’m at €9,323.

You’re probably wondering how a 16% profit leads me to extrapolate an annual return of nearly 100%. The answer is that I dramatically changed my leverage at the end of September… just in time for that ugly drawdown. If I was trading on my current leverage, the current live return would be around 40% (i.e., right on track to hit my goal).

What really counts is what I’ve really done. So far, I’m up €1,323 with another €40,677 to go by December 6, 2019.

The research for Dominari is effectively finished. It’s been slightly more than a year since I began researching the strategy. Although minor variations of Dominari popped up or came from traders copying my signals, none of them improved the long term outcomes.

One version that improved the risk profile was to trade with limit orders. The original blog post mentioned limit orders, but the variation placed them considerably further from the current market than what I tried previously. I also lacked a system for choosing settings appropriate to every pair, which I’ve more than likely resolved. The problem is that I have a million things on my to-do list and only 8 hours a day. You’ll see some of my top projects when you scroll down.

Pilum: The latest and greatest

Pilum is a strategy based on a statistical process that identifies momentum. One of the scary elements about most quantitative strategies is that most of them are mean-reverting. They buy when the price drops and sell when the price rises. The approach is favorable (i.e., profitable) in the long run, but it takes some psychological fortitude to trade.

Pilum is a major advancement because now I’ll have a strategy that should profit exactly when Dominari is most vulnerable to a drawdown.

The new strategy uses limit orders to enter the market. Something like 90% of these orders never execute. But when they do execute, I win 75% of the time. Additionally, my profile of winners to losers is very comfortable.

Most traders understand the ideas even if the statistical jargon is unfamiliar. Pilum’s biggest winner is larger than its biggest loss. The average winner is bigger than the average loser. And, it wins 77% of the time.

So far, I’ve done a sort of piecemeal backtest using R. When I finish the Quantilator (see below), I’ll redo the backtest in a fully fledged trading platform. More than likely, I’ll use QuantConnect to test the strategy level approach.

Trading platforms drive me crazy! The biggest problem that I have as a trader is continuously reallocating capital across my portfolio. MetaTrader, NinjaTrader and the likes implicitly assume that I want to trade some percentage of my account balance on every trade. Either that, or that I trade fixed lots.

Trading that way is extremely inefficient. I’m trying to trade 40+ currencies, so I need to be able to decide which ones need the money for trading and which ones don’t have signals. Then, among the ones that do have signals, I need to dish out their allocations proportionately. The allocations aren’t the same for each instrument. If you know of any good FX platforms that meet this requirement, then let me know in the comments section.

Testing Pilum on its own is important. More important than the performance of Pilum is how that performance interacts with Dominari. That means taking the daily equity values of each currency. Does Dominari lose when Pilum wins and vice versa? Should I allocate capital 50-50 between the strategies or does one strategy deserve the lion’s share of the portfolio? Is one strategy so good that it should get all of the money?

The main concern with portfolio allocation is how it relates to leverage. Dominari historically make 96% annual returns, inclusive of trading costs. But, it’s also trading with leverage of roughly 19:1. It’s possible for markets to rip over stops and create significant losses.

USDCHF lost 40% of its value in one hour in January 2015. Say that the scenario was even more extreme and that nobody could place a trade during that time at any price. That 40% move is multiplied by the 19x leverage used. That’s a theoretical 800% loss; more than the money in the account.

Everyone loves leverage because they think of profits. Leveraging losses is a lot less exciting.

So, if you could earn 96% annual returns and only use 5:1 leverage, that is exponentially superior to earning 96% on 19:1 leverage. I need to compare the returns of Pilum to Dominari per unit of risk. That allows me to do cool things like

Minimize the negative variance of the returns

Increase absolute return

Dynamically increase/decrease strategy allocations if I find patterns in their interactions

It’s a lower tech way of averaging strategies, like the litte guy’s version of what Numerai is doing… except that I have to create all of the strategies myself.

Quantilator

I spent the last few months sending surveys to segments of my subscribers asking how I can better serve you. Articles about indicators are overwhelmingly my most popular content. The trouble with that content is that I can’t complete the research fast enough to keep up.

The most valuable thing I’ve learned from the developing algorithms for the past 11 years is my process of deciding whether or not an indicator offers predictive value.

Say that you’re interested in gaps: do gaps predict future returns? What I normally do is code a gap indicator in R, implement my analysis methodology and give a verdict. My methodology is kind of like a system for building systems. Using my approach usually takes an hour for every new idea that comes along.

An hour is pretty short. An hour is REALLY short compared to when I didn’t have a research methodology. I used to waste months on junk strategies.

With Quantilator, I’ll be able to analyze anything in under 5 minutes. I’ll only have to follow 3 steps:

Run a script in MT4 to export price data and indicator data

Upload the exported data to Quantilator

Analyze the results

This tool will be 100% free. I’m planning to go through the most popular indicators in MetaTrader to analyze whether or not they offer an edge. I’m building a library of small edges that can be combined into powerful strategies like Dominari and Pilum. And, in the spirit of open source, I plan to make that library available to you for free.

I’m writing this tool in Django, which is a Python framework for displaying web content. The initial version will do the analysis. I’m hoping to use this as an education tool. A bit of momentum can justify make it a fully fledged library with sample data, indicators and training videos and more.

Quantilator’s name comes from a key concept in my system analysis methodology; I break data into quantiles. These quantiles calculate average market returns for a given period of time. The quant in Quantilator refers to quantiles, but I really like the implied double entendre of making you a quant. After all, that is what I’m doing for you!

This election is a colossal embarrassment. I don’t expect anything to go smoothly tomorrow and the media is priming everyone to believe that Clinton is all but assured. Markets hate surprises. If Trump looks competitive at any point tomorrow, then it can be a major catalyst for unwelcome volatility.

Most Tier 1 banks are reducing leverage ahead of the election. They rarely do that just for show. The chances of volatility popping sky-high are, well, sky-high. You’d do well to lower your market exposure accordingly.

A quick note for Dominari Traders

I’ve done the same in Dominari. I reduced my account leverage from 18:1 down to 3.5:1. It’s enough to keep me trading so that I can continue to monitor how Dominari performs. I’d rather miss out on profits that walk in front of a freight train.

Speaking of, I owe everyone a quick update on my trading performance.

You know what sucks about trading? Drawdowns.

You know what’s awesome about trading? Pushing out of drawdowns.

I took a solid punch on the nose right at the end of September, just as my copy-follow traders came on board with my new higher leverage. In a perfect storm for myself and them, I increased the leverage from 7:1 to 18:1 just in time for the drawdown to take hold.

What impressed me most was the 95% of the traders taking my signals stuck through the initial bumpy period. To them I say, “Bravo!”.

When trading, one of the most important pieces of information to have is the ability to identify momentum—when it begins and when it ends. It can help you plan your next trade and to ensure that that trade is successful. It is in the process of charting momentum that the Commodity Channel Index is especially effective, and that is regardless whether you are trading commodities, stocks or Forex.

The rationale behind the Commodity Channel Index or CCI is that it is an oscillator that measures the deviation from the simple moving average over the period. Just like most oscillators, it has an overbought level and an oversold.

In theory, using the CCI is similar to reading a Relative Strength Indicator (RSI). If the CCI is relatively high then the pair is overbought and when it is relatively low, the pair is oversold. In practice, however, using the CCI is a bit more complicated than the RSI. Unless the CCI is calibrated correctly it is practically worthless in identifying momentum cycles. Moreover, without correct calibration, it can generate plenty of false signals. But if you calibrated the CCI well it is an extremely efficient and powerful tool.

Commodity Channel Index: Calibration

The first step in calibrating the CCI is to identify when the current cycle began. This will help us decide the right period in which to run the CCI. In order to identify the beginning of the current cycle we can use Fibonacci Time Zones, which will give us an accurate measure.

For example, when we look at the Fibonacci Time Zones in the weekly chart below, we can conclude that the current cycle started 36 weeks ago. The rule of thumb is to divide the total period by three to give the average a bit more sensitivity. In the example below, it will be 36/3=12 weeks. That is the average the CCI should run on.

The reason we use Fibonacci Time Zones to calibrate the CCI is because the cycle’s length changes from wave to wave as they become longer and consequently the relevant average changes. Through the Fibonacci Time Zones, we can estimate with some degree of confidence when the cycle started.

Analysing the CCI

Once the CCI is calibrated, the rest is simple. The CCI, as previously mentioned, measures overbought and oversold levels. But rather than just looking at relative highs of the index we need to look at its behavior.

For example, in the Gold chart below, we can see that the CCI is converging with the price movement. That is a clear sign that bullish momentum is fading. If we take it a step further and continue our trend line all the way to the bottom, we can conclude another thing; that is that the pair, in this case XAU/USD, has already peaked and is heading lower in a bearish momentum.

Another way to chart the momentum is by examining the CCI behavior between the Fibonacci Time Zones. Notice that the CCI has a tendency to bottom out when the cycle ends and then rise. We can use that to ride on a rebound. There are cases, especially on a long term bullish trend, that we can get the exact opposite effect, i.e., the CCI peaks every time a Fibonacci Time Zone ends. The idea is to observe the pattern and then use it to your advantage.

Of course, as I’ve said in the past, oscillators should always be used alongside other indicators to get the full picture, and the Commodity Price Channel is no different. As usual in trading, there are no guarantees, but certainly the well calibrated CCI can provide a very coherent picture of where a pair’s momentum is headed, north or south.

Leicester City started the 2015 season with terrible odds of winning the Premier League Championship. Bookmakers only game them odds of 5,000:1 of winning.

To put that in context, you are more likely to die riding a bicycle than you were to win a bet on Leicester City. Or, you can think of betting on Leicester City every year. If you bet on them every single year for 5,000 years, you would expect them to win a grand total of… once.

2014 was hardly an indicator of their pending success. They were nearly relegated to a lower division (i.e., kicked out of the Premier League). And yet, they did win the championship last year.

Leicester City’s Biggest Fan

Meet John Michklethwait. He’s the former editor-in-cheif at The Economist and he’s currently editor-in-chief for Bloomberg. Clearly, he’s a very smart man. And yet, despite the odds and repeated disappointments, John bet on his old love, Leicester City, every single year dating back to the 1980s. That’s roughly 30 years of nonstop losing.

It wasn’t a lot of money each year: just £20. We all have our indulgences. I see the value of having skin in the game. £20 on a season is enough to make one care, but not so much that he’s upset about losing it.

Then something disruptive happened. John moved to the US last year for his position at Bloomberg. The chaos of the move threw him out of sorts, and he accidentally forgot to bet on Leicester City in 2015. He bet on them every single year dating back nearly 30 years. And yet the one year that he forgets to bet, not only did Leicester City win, but the bet paid out 5,000:1.

Let’s step back and calculate the cost of that oversight for Mr. Micklethwait.

£20 * 5,000 = £100,000.

A hundred… thousand… pounds. That kind of winning would put a nice dent in your mortgage, wouldn’t it?

The risk of low probability strategies

Everyone hears anecdotes about successful trend traders. Even winning only 30-40% of the time, they walk away big winners over time.

You live HERE. Math isn’t good enough. You also need to wonder if your strategy can handle real-world problems.

What if they took that even lower? They could move their stop losses closer to the market. They’d reduce the size of the average loser, but the winning percentage might also drop to 10-20%.

Mathematically, this could work out identically. 30% winners that earn 5x the average loser make for a profit factor of 1.5. A strategy with only 10% winners that make 15x the typical loser also have a 1.5 profit factor.

Mathematically, this could work out identically. 30% winners that earn 5x the average loser make for a profit factor of 1.5. A strategy with only 10% winners that make 15x the typical loser also have a 1.5 profit factor.

They’re the same. Aren’t they?

Planet Earth isn’t the same as planet Math. In the real world, people get sick and miss trades. Or, they move across the Atlantic and forget to place a £20 bet.

People move. They get sick. Computers break. Things can and will go wrong with trading.

Richard Dennis once commented that the Turtle Traders would often make their annual returns off of one, single trade. A single trade!

When your performance depends on positive outliers, you’re massively vulnerable to accidents. What happens if you’re sick that day? Or your internet goes down? Or your broker locks you out of your account on the worst possible day?

Life happens, brother. A plan that depends on perfection is no plan at all. You need to make yourself robust to failure. Or even better, you’d make yourself antifragile.

Winning percentages

I mentioned that you can do really well winning 30-40% of time. Why then, does my own trading strategy, Dominari, win 68% of the time?

Because I’m exploiting compound, exponential growth. It’s not just how much you win, but the order in which you win it.

Let’s take two examples:

A ranging strategy with a profit factor of 1.3 that wins 68% of the time.

A trending strategy with a profit factor of 1.3 that wins 30% of the time.

Look at the red circles. Trending strategies are prone to extreme outcomes, both positive and negative.

Each strategy risks about 1% on any given trade. And, the average of the range and trend strategies are identical in the long run.

But… and this is an important “but”, the expected worst case scenario with the trending strategy is substantially more likely compared to the range trading strategy. In effect, the average is more average with a ranging strategy than with a trending strategy.

Why is that? Because unusual losing streaks are devastating to trending strategies. Have you ever had a losing streak? It happens to everyone.

By using a strategy with a higher winning percentage, you’re making yourself robust to streaks of losers. And, not to mention, your average length of a winning streak is considerably higher.

Even though you’re getting the same mathematical outcome, you’re making things much easier on your trading psychology when you adopt a strategy with a higher winning percentage.

Dominari & Exponential Growth

You may have thought to yourself, “68%? That’s kind of a strange number to pick.”

You’d be right. The choice of 68% winners was not a coincidence. It is, in fact, the win rate on my Dominari strategy.

Dominari is about more than just buying and selling. Trading is also about managing a portfolio and position sizing. Position sizing is phenomenally important over your trading career.

My backtest results for Dominari show that for every $2,500, the account increased to $17,855.35 after 3 years. That kind of compound growth doesn’t happen by accident. That’s why I’d like to share the good news with you in my webinar this week.

I’m going to show you how to put that exponential awesomeness to work in your trading account. Sound good? Click here to register for the FREE webinar.

You’re about to ride a bullish trend; you plan your stop loss and gauge how much you can risk. You also know the rule of thumb—that is that your profit should be at least twice the amount you are willing to risk. But how can you know if the trade you’re considering really has potential that is worth twice the risk? The Fibonacci Expansion is a great tool that allows you to assess the potential of a bullish trend, especially when used with other indicators.

Drawing the Fibonacci Expansion

The Fibonacci Expansion on a MetaTrader trend line has three dots yet only the first and third are really worth your focus. The first dot has to be placed at the beginning of the first wave in our expansion wave and the third dot should be placed at the beginning of the second wave.

Caution: One of the biggest pitfalls in the Fibonacci Expansion is the failure to recognize the second wave. The second wave can only be considered a second wave if it is higher than the first; if it did not create a new high, it’s either not the second wave or, worse, there’s no expansion.

After the Fibonacci Expansion has been drawn, we can see the various levels of possible resistance. It is important to notice that, indeed, the various levels of Fibonacci are acting as resistance levels, especially the 61.8% and the 161.8%. If the Fibonacci levels and resistance levels do not align on key levels, the Fibonacci Expansion was drawn incorrectly.

Setting your Limit

When the Fibonacci levels are overlaid, you can get an indication on possible targets for your trend and can decide accordingly on which level to place your limit. If the limit is more than twice the distance in pips to your stop loss, that is a confirmation that the trade is worthwhile.

Now you are left with a key decision: where is the potential limit for this trade? That will depend on your degree of conservatism, i.e. your risk threshold. For example, placing your limit at the 200% level is somewhat aggressive. If you have to place your limit on that level to gain twice what you are risking, you are taking quite a chance because there is no margin of safety. But if you set your limit at 161.8% and that gives you twice what you are risking, then there is more margin for safety, and the pair is more likely to fit the 161.8% level than it is the 200%. If the pair surges beyond the 200% level you can repeat the drawing process and stretch a new line and get a new potential target.

Rules to Remember

The biggest risk with Fibonacci, whether it’s expansion or retracement, is that if you stretch it wrong, your entire strategy can go wrong, including your potential target. One way to avoid such a pitfall is to use the second wave rule of thumb. Another way to minimize risk is to calibrate Fibonacci using Parabolic SAR. It is also important to remember that, just like any other trend or support line, the higher you go on the intervals – daily, weekly, monthly – the more accurate it is likely to be (and vice versa, of course). Lastly, and perhaps the most important thing, is the understanding that Fibonacci does not determine trend—YOU must determine whether or not the trend is, indeed, bullish before considering the Fibonacci expansion as accurate.

The Alligator Index, as its name suggests, is highly effective at biting down on trading opportunities, or more appropriately, momentum opportunities. The Alligator Indicator is comprised of three SMMAs, or three Simple Moving Averages, that run on averages. The three SMMAs are aptly named the Jaw, the Teeth and the Lips.

The Jaw, the slowest SMMA, runs on a 13-day period.

The Teeth, in the middle, is an SMMA that runs on 8-day period

And the Lips, the most important, is a 5-day SMMA and it is the fastest.

The logic is simple and resembles the moving averages cross strategy. The strategy has three different signals.

The Alligator Mouth Open Bullish—that is when the Lips are above the Teeth and the Teeth is above the Jaw. When the three SMMAs align this way it is a signal for a bullish momentum and a buy.

The Alligator Mouth Open Bearish—this is the exact opposite of the bullish signal, with the Lips below the Teeth and the Teeth below the Jaw.

The Alligator is Sleeping—if the price starts to move in a more horizontal manner or if the averages diverge, it is called an Alligator Sleeping, which is another way to say there’s no trend.

When to use the Alligator Indicator

The Alligator Index’ strength is in its unique quality. It is a momentum index and yet it is most effective over the long term rather than short term as may be expected with a momentum index.

“Eating” and that is a bearish trend. Naturally, an Alligator Eating Bullish signal would be the mirror of the bearish signal with the Lips on top.

When the Alligator is Sleeping

The reason that the Alligator Indicator Sleeping mode is somewhat more complicated is because it is more difficult to recognize and also because it could either be a signal to keep holding the position or to close it, depending upon the buildup.

Using the same chart but with a different overlay we can focus on the tools and rules that help us recognize a Sleeping Alligator.

The first notable sign our Alligator Indicator has gone to “Sleep” is a wave that moves horizontally, or at least more horizontally than the preceding wave. This is clearly demonstrated by the blue arrows overlaid on the chart. When the price wave moves more horizontally and as long as the Lips are not crossing the Jaw, it is a signal to hold your position rather than close it—the trend is not over.

The second sign, which is also a warning, is if the averages start to diverge and cross one another. The Lips crossing the Jaw is not only a sign of a Sleeping Alligator but also a sign to close the position.

An important pitfall to avoid could occur after the cross; the Lips returns to cross below while the wave is still horizontal, as seen in the final wave in the chart. Because the wave is still horizontal the Alligator is still sleeping and because it was a close sign, it means we should not have reopened a position.

It is also important to look at the length of the Alligator Sleeping Phase compared to the Eating Phase that preceded it. If the Alligator Sleeping Phase is much shorter, don’t expect a trend change. Only after the Alligator is sleeping longer than the preceding Alligator Eating wave can a change in trend be the logical assumption.

Finally, another important practice that will help you avoid an Alligator pitfall is to always use an Alligator Indicator in conjunction with an MACD indicator. If the Alligator was Sleeping and suddenly turns into an Alligator Eating Bearish signal (or Bullish Signal if it is a bullish trade), and the MACD suggests weakening momentum, then the Alligator might be sending a false signal and is therefore still in a Sleeping Phase.

Buying into a bullish trend on the dips is a popular long term strategy. You focus on the USD/JPY or any other pair that is on a long term bullish trend, wait for it to dip and jump in. You buy when the pair is low and just wait for the trend to continue.

Yet as simple as that may sound, too many traders come out bruised from buying on the dip. The dip turns deeper and deeper until it hits your stop loss and you get thrown out. And then, usually, Murphy’s Law kicks in, and shortly after the pair slices through your stop loss, lo and behold, it starts rising. The bullish trend is back, only you’re not riding it.

What you need is a way to figure out how deep the dip is so you can plan your entry and ride on the bullish trend without getting thrown out. One tactic I find to be effective is combining a Gann Fan with an Oscillator. This allows you to predict, with a greater degree of accuracy, the right time to jump in and buy on the dip.

Every Gann Fan Needs an Oscillator

The way a Gann Fan works is simple. You stretch the main Gann line as a trend line support across the trend (see illustration below) and the Gann Fan function gives you alternative trend lines above and below. The problem is that it’s hard to tell which alternative trend line will be the one to actually hold and allow the trend to continue, leaving you no better off than you were without the Gann Fan.

Nevertheless, if we combine the Gann Fan with an oscillator, the Gann Fan becomes much more accurate. Possible oscillators to use are the MACD, Stochastic Oscillator or, in our case, the Moving Average Oscillator. I find the RSI to be less effective in this case, but all those we previously mentioned and some others can work well, as long as you know how to use them.

Looking at the sample below, we can see the Gann Fan suggests three possible points for the trend to resume; A, B and C. Both A and B break and fail to hold but C holds. What makes Point C so special? Only in Point C does our Oscillator move from negative to positive, suggesting a change in momentum, and signaling that this is the true support line.

Avoid the Pitfalls

Of course, just as in every strategy, there are pitfalls that you should do your best to avoid. Here are a few tricks to avoid the most common.

Check your Gann: The first thing you’ve got to check (and recheck) is that you’ve drawn the Gann Fan main line on top of the major trend line. You must be sure to start your Gann stretch from where the trend has begun, which will help you recognize the main trend line, as illustrated in the sample below.

Don’t Use Gann for Short Term: While it’s possible to stretch a Gann Fan on short intervals of 1h or less, oscillators are much less effective at those levels. And because oscillators allow the Gann Fan to become more accurate, the lack of an oscillator makes the Gann Fan much less effective and warrants the use of a different set of strategies and tools altogether.

Beware of the Bears: One of the biggest risks of using a Gann Fan, even with oscillators, is that if the trend has changed from bullish to bearish the Gann levels won’t hold. If you failed to recognize the change in trend, a good warning sign is if the pair fails to break the fan above for 2 to 3 times. This is a sign that resistance for the pair is heavy. It could be wise to at least double check that the trend is still bullish and if you are not sure it is then it might be wise to eject and get out of the trade before it’s too late.